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Data-driven Prognostics and Remaining Useful Life Estimation for Lithium-ion Battery: A Review

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文献出处
Instrumentation  2014年01期
机 构
Department,of,Automatic,Test,and,Control,Harbin,Institute,of,Technology
基 金
supported,partly,by,National,Natural,Science,Foundation,of,China(Grant,No.61301205),Research,Fund,for,the,Doctoral,Program,of,Higher,Education,of,China(Grant,No.20112302120027),Natural,Scientific,Research,Innovation,Foundation,in,Harbin,Institute,of,Technology(Grant,No.HIT.NSRIF.2014017),China,Scholarship,Council.,2155-0875,Copyright,C,2010,Binary,Information,Press,July,2010
论文摘要

As an important and necessary part in the intelligent battery management systems(BMS),the prognostics and remaining useful life(RUL) estimation for lithiumion batteries attach more and more attractions. Especially,the data-driven approaches use only the monitoring data and historical data to model the performance degradation and assess the health status,that makes these methods flexible and applicable in actual lithiumion battery applications. At first,the related concepts and definitions are introduced. And the degradation parameters identification and extraction is presented,as the health indicator and the foundation of RUL prediction for the lithiumion batteries. Then,data-driven methods used for lithiumion battery RUL estimation are summarized,in which several statistical and machine learning algorithms are involved. Finally,the future trend for battery prognostics and RUL estimation are forecasted.

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